Paper
In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks
Authors
Shangqing Xu, Harshavardhan Kamarthi, Haoxin Liu, B. Aditya Prakash
Abstract
Time-series foundation models (TSFMs) have demonstrated strong generalization capabilities across diverse datasets and tasks. However, existing foundation models are typically pre-trained to enhance performance on specific tasks and often struggle to generalize to unseen tasks without fine-tuning. To address this limitation, we propose augmenting TSFMs with In-Context Learning (ICL) capabilities, enabling them to perform test-time inference by dynamically adapting to input-output relationships provided within the context. Our framework, In-Context Time-series Pre-training (ICTP), restructures the original pre-training data to equip the backbone TSFM with ICL capabilities, enabling adaptation to unseen tasks. Experiments demonstrate that ICT improves the performance of state-of-the-art TSFMs by approximately 11.4% on unseen tasks without requiring fine-tuning.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.20307v1</id>\n <title>In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks</title>\n <updated>2026-02-23T19:48:47Z</updated>\n <link href='https://arxiv.org/abs/2602.20307v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.20307v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Time-series foundation models (TSFMs) have demonstrated strong generalization capabilities across diverse datasets and tasks. However, existing foundation models are typically pre-trained to enhance performance on specific tasks and often struggle to generalize to unseen tasks without fine-tuning. To address this limitation, we propose augmenting TSFMs with In-Context Learning (ICL) capabilities, enabling them to perform test-time inference by dynamically adapting to input-output relationships provided within the context. Our framework, In-Context Time-series Pre-training (ICTP), restructures the original pre-training data to equip the backbone TSFM with ICL capabilities, enabling adaptation to unseen tasks. Experiments demonstrate that ICT improves the performance of state-of-the-art TSFMs by approximately 11.4% on unseen tasks without requiring fine-tuning.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <published>2026-02-23T19:48:47Z</published>\n <arxiv:primary_category term='cs.LG'/>\n <author>\n <name>Shangqing Xu</name>\n </author>\n <author>\n <name>Harshavardhan Kamarthi</name>\n </author>\n <author>\n <name>Haoxin Liu</name>\n </author>\n <author>\n <name>B. Aditya Prakash</name>\n </author>\n </entry>"
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